Search (132 results, page 2 of 7)

  • × theme_ss:"Retrievalalgorithmen"
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  1. Wechsler, M.; Schäuble, P.: ¬The probability ranking principle revisited (2000) 0.01 
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    Source
    Information retrieval. 3(2000), S.217-227
  2. Liddy, E.D.; Diamond, T.; McKenna, M.: DR-LINK in TIPSTER (2000) 0.01 
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    Source
    Information retrieval. 3(2000), S.291-311
  3. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22
  4. Kang, I.-H.; Kim, G.C.: Integration of multiple evidences based on a query type for web search (2004) 0.01 
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    Abstract
    The massive and heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web pages are not enough to find answer pages. PageRank compensates for the insufficiencies of content information. The content information and PageRank are combined to get better results. However, static combination of multiple evidences may lower the retrieval performance. We have to use different strategies to meet the need of a user. We can classify user queries as three categories according to users' intent, the topic relevance task, the homepage finding task, and the service finding task. In this paper, we present a user query classification method. The difference of distribution, mutual information, the usage rate as anchor texts and the POS information are used for the classification. After we classified a user query, we apply different algorithms and information for the better results. For the topic relevance task, we emphasize the content information, on the other hand, for the homepage finding task, we emphasize the Link information and the URL information. We could get the best performance when our proposed classification method with the OKAPI scoring algorithm was used.
    Source
    Information processing and management. 40(2004) no.3, S.459-478
  5. Liu, A.; Zou, Q.; Chu, W.W.: Configurable indexing and ranking for XML information retrieval (2004) 0.01 
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    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
  6. Yu, K.; Tresp, V.; Yu, S.: ¬A nonparametric hierarchical Bayesian framework for information filtering (2004) 0.01 
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    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
  7. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.01 
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    Source
    Information processing and management. 37(2001) no.4, S.623-637
  8. Horng, J.T.; Yeh, C.C.: Applying genetic algorithms to query optimization in document retrieval (2000) 0.01 
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    Source
    Information processing and management. 36(2000) no.5, S.737-759
  9. Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004) 0.01 
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    Source
    Information processing and management. 40(2004) no.6, S.869-
  10. Kwok, K.L.: Improving English and Chinese ad-hoc retrieval : a TIPSTER text phase 3 project report (2000) 0.01 
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    Source
    Information retrieval. 3(2000), S.313-338
  11. Clarke, C.L.A.; Cormack, G.V.; Tudhope, E.A.: Relevance ranking for one to three term queries (2000) 0.01 
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    Source
    Information processing and management. 36(2000) no.2, S.291-311
  12. Chung, Y.M.; Lee, J.Y.: Optimization of some factors affecting the performance of query expansion (2004) 0.01 
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    Source
    Information processing and management. 40(2004) no.6, S.891-
  13. Losee, R.M.; Church Jr., L.: Are two document clusters better than one? : the cluster performance question for information retrieval (2005) 0.01 
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    Abstract
    When do information retrieval systems using two document clusters provide better retrieval performance than systems using no clustering? We answer this question for one set of assumptions and suggest how this may be studied with other assumptions. The "Cluster Hypothesis" asks an empirical question about the relationships between documents and user-supplied relevance judgments, while the "Cluster Performance Question" proposed here focuses an the when and why of information retrieval or digital library performance for clustered and unclustered text databases. This may be generalized to study the relative performance of m versus n clusters.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.1, S.106-108
  14. Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003) 0.01 
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    Source
    Journal of information science. 29(2003) no.2, S.87-96
  15. García Cumbreras, M.A.; Perea-Ortega, J.M.; García Vega, M.; Ureña López, L.A.: Information retrieval with geographical references : relevant documents filtering vs. query expansion (2009) 0.01 
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    Abstract
    This is a thorough analysis of two techniques applied to Geographic Information Retrieval (GIR). Previous studies have researched the application of query expansion to improve the selection process of information retrieval systems. This paper emphasizes the effectiveness of the filtering of relevant documents applied to a GIR system, instead of query expansion. Based on the CLEF (Cross Language Evaluation Forum) framework available, several experiments have been run. Some based on query expansion, some on the filtering of relevant documents. The results show that filtering works better in a GIR environment, because relevant documents are not reordered in the final list.
    Source
    Information processing and management. 45(2009) no.5, S.605-614
  16. Maron, M.E.: ¬An historical note on the origins of probabilistic indexing (2008) 0.01 
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    Abstract
    The motivation behind "Probabilistic Indexing" was to replace two-valued thinking about information retrieval with probabilistic notions. This involved a new view of the information retrieval problem - viewing it as problem of inference and prediction, and introducing probabilistically weighted indexes and probabilistically ranked output. These ideas were first formulated and written up in August 1958.
    Source
    Information processing and management. 44(2008) no.2, S.971-972
  17. Hubert, G.; Mothe, J.: ¬An adaptable search engine for multimodal information retrieval (2009) 0.01 
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    Abstract
    This article describes an information retrieval approach according to the two different search modes that exist: browsing an ontology (via categories) or defining a query in free language (via keywords). Various proposals offer approaches adapted to one of these two modes. We present a proposal leading to a system allowing the integration of both modes using the same search engine. This engine is adapted according to each possible search mode.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.8, S.1625-1634
  18. Beitzel, S.M.; Jensen, E.C.; Chowdhury, A.; Grossman, D.; Frieder, O; Goharian, N.: Fusion of effective retrieval strategies in the same information retrieval system (2004) 0.01 
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    Abstract
    Prior efforts have shown that under certain situations retrieval effectiveness may be improved via the use of data fusion techniques. Although these improvements have been observed from the fusion of result sets from several distinct information retrieval systems, it has often been thought that fusing different document retrieval strategies in a single information retrieval system will lead to similar improvements. In this study, we show that this is not the case. We hold constant systemic differences such as parsing, stemming, phrase processing, and relevance feedback, and fuse result sets generated from highly effective retrieval strategies in the same information retrieval system. From this, we show that data fusion of highly effective retrieval strategies alone shows little or no improvement in retrieval effectiveness. Furthermore, we present a detailed analysis of the performance of modern data fusion approaches, and demonstrate the reasons why they do not perform weIl when applied to this problem. Detailed results and analyses are included to support our conclusions.
    Source
    Journal of the American Society for Information Science and Technology. 55(2004) no.10, S.859-868
  19. Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005) 0.01 
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    Abstract
    The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.1, S.63-69
  20. Henzinger, M.R.: Link analysis in Web information retrieval (2000) 0.01 
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    Abstract
    The analysis of the hyperlink structure of the web has led to significant improvements in web information retrieval. This survey describes two successful link analysis algorithms and the state-of-the art of the field.
    Content
    The goal of information retrieval is to find all documents relevant for a user query in a collection of documents. Decades of research in information retrieval were successful in developing and refining techniques that are solely word-based (see e.g., [2]). With the advent of the web new sources of information became available, one of them being the hyperlinks between documents and records of user behavior. To be precise, hypertexts (i.e., collections of documents connected by hyperlinks) have existed and have been studied for a long time. What was new was the large number of hyperlinks created by independent individuals. Hyperlinks provide a valuable source of information for web information retrieval as we will show in this article. This area of information retrieval is commonly called link analysis. Why would one expect hyperlinks to be useful? Ahyperlink is a reference of a web page B that is contained in a web page A. When the hyperlink is clicked on in a web browser, the browser displays page B. This functionality alone is not helpful for web information retrieval. However, the way hyperlinks are typically used by authors of web pages can give them valuable information content. Typically, authors create links because they think they will be useful for the readers of the pages. Thus, links are usually either navigational aids that, for example, bring the reader back to the homepage of the site, or links that point to pages whose content augments the content of the current page. The second kind of links tend to point to high-quality pages that might be on the same topic as the page containing the link.

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